Suppression subtractive hybridization profiles of radial growth phase and metastatic melanoma cell lines reveal novel potential targets

Article (PDF Available)inBMC Cancer 8(1):19 · February 2008with17 Reads
DOI: 10.1186/1471-2407-8-19 · Source: PubMed
Melanoma progression occurs through three major stages: radial growth phase (RGP), confined to the epidermis; vertical growth phase (VGP), when the tumor has invaded into the dermis; and metastasis. In this work, we used suppression subtractive hybridization (SSH) to investigate the molecular signature of melanoma progression, by comparing a group of metastatic cell lines with an RGP-like cell line showing characteristics of early neoplastic lesions including expression of the metastasis suppressor KISS1, lack of alphavbeta3-integrin and low levels of RHOC. Two subtracted cDNA collections were obtained, one (RGP library) by subtracting the RGP cell line (WM1552C) cDNA from a cDNA pool from four metastatic cell lines (WM9, WM852, 1205Lu and WM1617), and the other (Met library) by the reverse subtraction. Clones were sequenced and annotated, and expression validation was done by Northern blot and RT-PCR. Gene Ontology annotation and searches in large-scale melanoma expression studies were done for the genes identified. We identified 367 clones from the RGP library and 386 from the Met library, of which 351 and 368, respectively, match human mRNA sequences, representing 288 and 217 annotated genes. We confirmed the differential expression of all genes selected for validation. In the Met library, we found an enrichment of genes in the growth factors/receptor, adhesion and motility categories whereas in the RGP library, enriched categories were nucleotide biosynthesis, DNA packing/repair, and macromolecular/vesicular trafficking. Interestingly, 19% of the genes from the RGP library map to chromosome 1 against 4% of the ones from Met library. This study identifies two populations of genes differentially expressed between melanoma cell lines from two tumor stages and suggests that these sets of genes represent profiles of less aggressive versus metastatic melanomas. A search for expression profiles of melanoma in available expression study databases allowed us to point to a great potential of involvement in tumor progression for several of the genes identified here. A few sequences obtained here may also contribute to extend annotated mRNAs or to the identification of novel transcripts.
BioMed Central
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BMC Cancer
Open Access
Research article
Suppression subtractive hybridization profiles of radial growth
phase and metastatic melanoma cell lines reveal novel potential
Josane F Sousa and Enilza M Espreafico*
Address: Department of Cellular and Molecular Biology and Pathogenic Bioagents of the Faculty of Medicine of Ribeirão Preto – University of São
Paulo, Ribeirão Preto, SP, Brazil
Email: Josane F Sousa -; Enilza M Espreafico* -
* Corresponding author
Background: Melanoma progression occurs through three major stages: radial growth phase
(RGP), confined to the epidermis; vertical growth phase (VGP), when the tumor has invaded into
the dermis; and metastasis. In this work, we used suppression subtractive hybridization (SSH) to
investigate the molecular signature of melanoma progression, by comparing a group of metastatic
cell lines with an RGP-like cell line showing characteristics of early neoplastic lesions including
expression of the metastasis suppressor KISS1, lack of αvβ3-integrin and low levels of RHOC.
Methods: Two subtracted cDNA collections were obtained, one (RGP library) by subtracting the
RGP cell line (WM1552C) cDNA from a cDNA pool from four metastatic cell lines (WM9,
WM852, 1205Lu and WM1617), and the other (Met library) by the reverse subtraction. Clones
were sequenced and annotated, and expression validation was done by Northern blot and RT-PCR.
Gene Ontology annotation and searches in large-scale melanoma expression studies were done for
the genes identified.
Results: We identified 367 clones from the RGP library and 386 from the Met library, of which
351 and 368, respectively, match human mRNA sequences, representing 288 and 217 annotated
genes. We confirmed the differential expression of all genes selected for validation. In the Met
library, we found an enrichment of genes in the growth factors/receptor, adhesion and motility
categories whereas in the RGP library, enriched categories were nucleotide biosynthesis, DNA
packing/repair, and macromolecular/vesicular trafficking. Interestingly, 19% of the genes from the
RGP library map to chromosome 1 against 4% of the ones from Met library.
Conclusion: This study identifies two populations of genes differentially expressed between
melanoma cell lines from two tumor stages and suggests that these sets of genes represent profiles
of less aggressive versus metastatic melanomas. A search for expression profiles of melanoma in
available expression study databases allowed us to point to a great potential of involvement in
tumor progression for several of the genes identified here. A few sequences obtained here may
also contribute to extend annotated mRNAs or to the identification of novel transcripts.
Published: 22 January 2008
BMC Cancer 2008, 8:19 doi:10.1186/1471-2407-8-19
Received: 17 July 2007
Accepted: 22 January 2008
This article is available from:
© 2008 Sousa and Espreafico; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
BMC Cancer 2008, 8:19
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Melanoma arises from melanocytes, specialized cells in
the skin responsible for synthesizing and distributing the
pigment melanin. This tumor is one of the most aggres-
sive malignancies, marked by elevated capacity to metas-
tasize and by high drug resistance [for review see [1-3]].
Melanoma often arises from inherited or simple acquired
nevi, which are pigmentary melanocytic lesions that can
progress through hyperplastic to dysplastic nevi and cul-
minate in some cases in the radial growth phase
melanoma (RGP), an early melanoma lesion that is con-
fined to the epidermis [for review see [4]]. This lesion usu-
ally further progress to a vertical growth phase (VGP)
melanoma, in which the cells that were growing only lat-
erally in the epidermis become able to invade into the der-
mis and acquire metastatic potential [for review see [4]].
The establishment of metastasis is believed to require few
additional genetic changes, once cells presenting meta-
static phenotype can be readily selected from most VGP
melanomas [5].
Cell lines derived from RGP, VGP and metastatic
melanoma represent an interesting experimental model
for identification and characterization of genes involved
in melanoma development, since they sustain in vitro the
characteristics representing the original state of the tumor
stage from which they are derived [6,7]. RGP cell lines
usually mimic early, less aggressive melanoma lesions,
since they show low anchorage-independent growth, high
growth factor dependency, and are non-tumorigenic or
have limited ability to induce tumor in immunodefficient
mice [6,8]. VGP lesions usually contain cells that have
already acquired metastatic capacity and so they show
behavior and expression profiles similar to cells from
metastasis [4,9]. However, since the tumor lesions are het-
erogeneous, some cells derived from VGP tumors can still
sustain a less aggressive phenotype [5,10]. The heteroge-
neity of VGP cells was strengthened by the result of a
microarray study using melanoma cell lines, in which
some VGP cells clustered with metastatic melanoma
whereas others did so with RGP cells [11].
In spite of the fact that different lines of evidence support
the notion that cancer progresses through discrete pheno-
typic stages marked by a stepwise acquisition of oncogenic
alterations, recent evidence from high-throughput gene
expression studies in cancer [12] lead to an emerging par-
adigm that tumor aggressiveness is intrinsically associated
to the mechanisms of tumor birth [13]. From this point of
view, higher proclivity towards a metastatic phenotype
would be inherent to the initial set of genetic alterations
that generate a tumor. The tendency of melanoma to gen-
erate metastasis may as well be corroborated by the fact
that this tumor derives from melanocytes, naturally
migratory neural crest descendants, as suggested by Gupta
et al. [14]. The notion that a metastatic melanoma may
arise from an RGP lesion has been also supported by clin-
ical and molecular evidence [15,16], strengthening the
importance of determining molecular alterations that dis-
tinguish particularly less aggressive melanoma cells from
metastatic cells as an approach to identify molecular
events that drive the selection towards one of these phe-
Many genes with altered expression associated to
melanoma progression have been identified [3,4]. A nota-
ble example of a molecular marker of the transition from
RGP to VGP melanomas is the β3 subunit of the αvβ3
integrin, a vitronectin receptor. The expression of β3
integrin is detected in most VGP and metastatic melano-
mas, whereas normal melanocytes and RGP melanomas
do not express this integrin subunit [17]. The expression
of β3 integrin has been shown to contribute to metastatic
phenotype by altering the adhesion and promoting sur-
vival of melanoma cells [8,18,19]. Another example of a
gene involved in the transition from RGP to VGP is KISS1,
which has been postulated as a metastasis suppressor gene
[20,21]. KISS1 encodes the protein kisspeptin-1/metastin
that was identified as the endogenous ligand for the G
protein-coupled receptor GPR54/KISS1R [22,23], and has
been shown to play an anti-migratory role in vitro and to
act as a metastasis inhibitor in vivo [20]. KISS1 expression
is detected in normal melanocyte and RGP melanomas,
but its expression is lost in VGP and metastatic cells
[20,21]. Also, in other cancer types such as breast, bladder
and pancreatic cancer, loss or reduced expression of KISS1
has been associated to the metastatic phenotype [24-26].
On the other hand, RHOC has been identified as an over-
expressed gene in metastatic murine and human
melanoma cells in comparison with the non-metastatic
parental cells [27]. RHOC, like the other RHO family pro-
teins, is involved in the regulation of the actin cytoskele-
ton dynamics and overexpression of RHOC induces the
cells to become highly metastatic by enhancing their
migratory and invasive capacities [27].
Although many cancer-related genes have been character-
ized, several lines of evidence suggest that many more
remain to be identified. Present estimate has indicated
that around 1% of the genes in the human genome are
involved in cancer and there are predictions that 5–10%
or more can contribute to oncogenesis [28]. Suppression
subtractive hybridization is a widely used method for sep-
arating mRNA sequences that distinguish two mRNA pop-
ulations [29]. A key feature of the method is the
simultaneous normalization and subtraction steps. The
normalization step equalizes the abundance of mRNA
within the target population, and the subtraction step
excludes sequences that are common to the two popula-
tions being compared. The SSH methodology allows the
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detection of low copy transcripts and, in contrast to
microarray analysis, it allows the identification of
unknown genes or non-coding RNAs, thus representing
an alternative and complementary approach for differen-
tial expression analyses (for a comparative study see [30]).
In the present work, we used SSH approach to compare a
non-tumorigenic cell line whose behavior and expression
profile of a particular set of genes are suggestive of low
aggressiveness to a pool of established, highly tumori-
genic and metastatic cell lines, aiming to identify sets of
genes potentially involved in maintaining low versus high
aggressiveness status in melanoma.
Cell culture
The cell lines used in this work represent the three major
stages of melanoma progression: radial growth phase
(WM35, WM1552C and WM1789), vertical growth phase
(WM278, WM793 and WM902) and metastasis (WM9,
WM852, 1205Lu and WM1617). All melanoma cell lines
were kindly provided by Dr. Meenhard Herlyn (Wistar
Institute, Philadelphia, PA). The cells were maintained in
melanoma medium, consisting of four parts of MCDB153
(Sigma, Saint Louis, MO, USA) and one part of L-15 (Inv-
itrogen, Carlsbard, CA, USA), supplemented with 2 mM
, 5 μg/ml insulin and 2% fetal bovine serum (Invit-
rogen, Carlsbard, CA, USA).
Isolation of RNA and mRNA
Total RNA was isolated using Trizol reagent (Invitrogen,
Carlsbard, CA, USA) and mRNA was isolated from total
RNA using the Oligotex™ mRNA kit (Qiagen, Valencia,
CA, USA) according to manufacturer's instructions. The
integrity of RNA and mRNA was checked on a 1% formal-
dehyde agarose gel.
Suppression subtractive hybridization (SSH)
The subtractive libraries were constructed using the Clon-
tech PCR-Select™ cDNA Subtraction kit (Clontech, Palo
Alto, CA, USA). Briefly, 1 μg of mRNA (poly dA
from WM1552C (RGP-like) and equal amount from a
pool of four metastatic cell lines (WM9, WM852, 1205Lu
and WM1617) were used for double strand cDNA synthe-
sis, and the resulting cDNA was digested with Rsa I. For
the RGP-library, the digested cDNA from WM1552C (as a
Tester) was split into two groups and linked to either
adaptor I or adaptor 2R. Subtractive hybridization was
performed by annealing an excess of the metastatic cell
cDNA (as a Driver) with each sample of adaptor-ligated
tester cDNA. The cDNAs were heat denatured and incu-
bated at 68°C for 8 hours (h). After the first hybridization,
the two samples were mixed together and hybridized
again with freshly denatured driver cDNA for 20 h at
68°C. The two rounds of hybridization would generate a
normalized population of tester specific cDNAs with dif-
ferent adaptors on each end. After filling in the ends, two
rounds of PCR amplification were performed to enrich for
the desired cDNAs containing both adaptors. The opti-
mized cycling for the first and second PCR rounds, to
increase representation and reduce redundancy of sub-
tracted cDNAs, were 27 and 10 cycles, respectively. The
Met-library was constructed using the same approach but
with cDNA from the metastatic cell lines as a Tester and
cDNA from WM1552C as a Driver.
Cloning of the subtracted cDNAs
The amplified products containing the subtracted cDNAs
from both subtraction processes (4 μL) were independ-
ently ligated into a pGEM-Teasy vector (Promega Co.,
USA) and transformed into E. coli strain DH5α. Bacteria
were supplied with 800 μL of SOB medium, incubated for
1 h at 37°C, and subsequently plated onto agar plates
containing 100 μg/mL ampicillin, 100 mM IPTG and 100
mg/mL X-gal at 37°C, for 20 h. White colonies were inoc-
ulated into 96-well plates containing 150 μL of 2× YT liq-
uid medium supplemented with 100 μg/mL ampicillin.
The cultures were grown overnight, without shaking, at
37°C. PCR amplification to check for the positive clones,
i.e., to confirm the presence of insert, and to generate
sequencing templates was performed as previously
described [31].
Sequencing, annotation and sequence analysis
A total of 753 clones from both libraries were sequenced
using the kit DYEnamic ET dye terminator cycle sequenc-
ing (Amersham-Pharmacia, Pollards Wood, UK) and a
M13 primer in the capillary DNA sequencer Megabace
1000 (Amersham-Pharmacia Biotech, Pollards Wood,
UK). The BLAST program was used to search for the cDNA
sequence similarity of isolated clones in the GenBank
[32]. Annotated sequences were submitted to functional
annotation according to the Gene Ontology database,
using the tool GOTM-Gene Ontology Tree Machine [33].
For the chromosome distribution analysis, chromosome
locations of all genes/ESTs were obtained from GenBank
accession number reports or through BLAT alignment
[34], and then the total number of genes per human chro-
mosome for each library was plotted in a bar graphic.
Graphics showing the gene distribution along each
human chromosome was generated using the "Chromo-
somal Distribution Chart" tool from the WebGestalt
home page [35].
Northern blot
For Northern blot preparation, 20 μg of total RNA was
separated by 1% formaldehyde-agarose gel electrophore-
sis and transferred to nylon membrane (Hybond N, Amer-
sham Pharmacia Biotech, Pollards Wood, UK) by
standard methods. RNA was fixed to membrane by baking
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the blot and by UV cross-linking. Pre-hybridization was
done in a solution containing 7% SDS, 1% BSA, 1 mM
EDTA, and 0.5 M NaHPO
pH 7.5 [36], at 65°C for at least
1 h, in a 30/3,5 cm roller bottle in a hybridization oven.
For probe generation, digested inserts were gel purified
(Qiaex II kit-Qiagen, Valencia, CA) and about 50 ng were
radio-labeled with [α-32P]-dCTP by random-priming
(Rad-prime kit, Invitrogen, Carlsbad, CA, USA). Unincor-
porated nucleotides were removed by gel filtration
through a G-50 Sephadex column. The hybridization was
performed for 18 h using the probe to 1 × 10
hybridization solution. The blots were washed in the fol-
lowing manner: one time in 2 × SSC, 0,2% SDS, for 5 min,
at room temperature; two times in 1 × SSC, 0,2% SDS, for
30 min each, at 65°C; and one time in 0,2 × SSC, 0,2%
SDS, for 30 min, at 65°C. Then, the blots were covered in
clear plastic wrap and exposed to a Phosphoimager screen
(Molecular Dynamics, Piscataway, NJ, USA). In order to
correct for different lane loads, blots were stripped at
100°C in 0.5% SDS and probed with a fragment for ACTB
(β actin) gene.
RT-PCR for HLA-DRA gene
For RT-PCR, total RNA was treated with DNase I
(Promega, Madison, WI, USA) at 1 U/2 μg of total RNA in
10 μL reaction volume and incubated for 30 min at 37°C,
followed by enzyme inactivation by addition of 1 μL of 20
mM EDTA and incubation for 15 min, at 65°C. cDNA
synthesis was performed using 2 μg of total RNA in 20 μL
reaction with Superscript II Reverse Transcriptase (Invitro-
gen, Carlsbad, CA, USA), according to the manufacture's
instructions, using 4 μL of 5× first-strand buffer, 1 μL of 10
mM dNTP, 200 U Superscript II enzyme, 2 μL of 0.1 M
DTT, and 250 ng oligo dT primer (Invitrogen, Carlsbad,
CA, USA). For PCR reactions, 1 μL of each synthesized
cDNA was used as template in a reaction volume of 50 μL
containing 200 μM dNTPs, 1,5 mM MgCl
, 0.25 μM each
primer, and 1 U Taq DNA polymerase in the manufac-
ture's recommended buffer (Invitrogen, Carlsbad, CA,
USA). The reaction was allowed to denature for 4 min at
94°C, followed by amplification (25, 28, 30 e 32 cycles:
45 s at 94°C, 1 min at 55°C, 1 min at 72°C). At indicated
cycles, a 5 μL sample was colleted from each reaction.
Amplification of ACTB (β actin) cDNA was done as con-
trol for mRNA content. The following forward (F) and
reverse (R) primers were used: F-ACAGAGCGCCCAA-
amplification of HLA-DRA; and F-GGCATCGTGAT-
PCR products were loaded onto a 1% agarose gel and elec-
trophoresed in TAE buffer. Gels were subjected to ethid-
ium bromide staining and were imaged in a UV
transilluminator using a digital Kodak camera.
Analysis of the expression profile of genes represented by
subtractive clones in a publicly available microarray study
of melanoma samples
We downloaded from the PNAS website [37] the table
number 10 containing the normalized and log
formed expression data from the microarray study
described by Haqq et al [16]. This table presents the data
of the comparison of expression profiles of samples from
normal skin, nevi, primary and metastatic melanomas
using a microarray from Research Genetics containing
20,862 human cDNA clones. Using a locally developed
computer script, we extracted the expression data from
their microarray analysis for all genes that were also repre-
sented in both of our subtractive libraries (RGP and Met).
The expression values of both groups of genes were sub-
mitted to SAM (Significance Analysis of Microarrays) [38]
software in a two class comparison, first to detect the
genes presenting differential expression between primary
and metastatic melanomas and then, in a extended analy-
sis, between non-neoplastic tissues (skin and nevi) and
tumors (primary and metastatic melanomas). The results
of SAM were extracted using the software SAMSTER [39],
submitted to hierarchical clustering using CLUSTER and
then visualized by JAVATREEVIEW [40].
Selection of cell lines for generation of two cDNA SSH
libraries and sequencing analysis
We used the SSH approach to identify populations of
mRNA that distinguish between a non-tumorigenic RGP
cell line (WM1552C) and a pool of four metastatic cell
lines (WM9, WM852, WM1617, 1205Lu). In order to
reduce individual genetic variations, we initially aimed to
use a pool of less aggressive cell lines as we do for the met-
astatic cells. However, as shown here based on several cri-
teria we failed to find more than one among six RGP/VGP
cell lines tested to fit in a "less aggressive" phenotype.
Also, we were discouraged to include a VGP cell line in the
study based on the rationale that cells from VGP tumors
are more heterogeneous, as pointed out in the Back-
ground section.
For selecting the cell lines, we checked on the expression
of three known molecular markers of melanoma progres-
sion, KISS1 and RHOC mRNAs and the αvβ3 integrin, in
the panel of melanoma cell lines used here. KISS1 mRNA
expression was detected by Northern blot only in the RGP
cell line WM1552C (Fig. 1A). So even the other two RGP
cell lines (WM35 and WM1789) included in our study
failed to show detectable levels of KISS1 mRNA, although
by using a more sensitive method (RT-PCR/Southern blot-
ting), a weak expression of KISS1 transcript in the WM35
cell line in contrast with lack of expression in WM793
(VGP) and 1205Lu (metastatic) was previously reported
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The RHOC mRNA was detected in all melanoma cell lines,
presenting an elevated expression particularly in the RGP
cell line WM1789 (Fig. 1B). Also, WM1552C was previ-
ously shown to lack expression of αvβ3 integrin [8] in
contrast to WM793 and the metastatic cell lines WM9 and
1205Lu [41]. Here, we confirmed by flow cytometry that
WM1552C cells do not express αvβ3 integrin, while we
observed expression of this integrin in WM35 (RGP),
WM278 (VGP) and WM1617 metastatic cells (data not
shown). In addition, we found that expression of these
molecular markers was compatible with observations
made in our laboratory (unpublished data) that
WM1552C is more sensitive to apoptosis triggered by cell
adhesion impairment (anoikis) than WM35 cells and that
both WM35 and WM1789 cell lines were capable to gen-
erate slowly growing tumor in SCID mice, in contrast with
WM1552C that in a preliminary assay was unable to
induce visible primary tumor when injected (2 × 10
into SCID mice in the same conditions. Furthermore,
spontaneous transformation towards a more malignant
phenotype has been pointed out for WM35 cell line [7]
and, indeed, in contrast to WM1552C, WM35 cells were
recently shown to express the melanoma chondroitin sul-
fate proteoglycan (MCSP), a surface molecule implicated
in enhanced tumor migration, invasion and anchorage-
independent survival [42]. In view of these contrasts,
although WM1552C cells carry the BRAF mutation
V599E, they appears to retain the phenotype of a less
aggressive melanoma tumor as compared with the other
cell lines of this collection and therefore it was the only
cell line selected as representative of the RGP stage for this
Two subtracted cDNA collections were obtained, one of
cDNA from the RGP cell line WM1552C subtracted from
a cDNA pool of four metastatic cell lines (WM9, WM852,
1205Lu and WM1617), which we named RGP library. The
second library, referred as Met library, was obtained by the
reverse subtraction. The cDNA profiles generated by the
subtraction process are shown in Fig. 2. Cloning of these
cDNAs into the pGEM vector allowed us to obtain 2016
clones for the RGP library and 1920 clones for the Met
library. PCR analysis of 395 and 336 randomly selected
clones from the RGP and Met libraries, respectively, indi-
cated that 97% of the clones from the RGP library and
98% from the Met library contained inserts and that the
insert size of most clones was 600 bp (images of repre-
sentative agarose gels for each library are shown in the
Additional File 1, Fig. S1).
A total of 753 clones from both libraries were sequenced
and annotated, as summarized in Table 1. The sequences
were submitted to GenBank [GenBank accession num-
bers: ES315683
–ES316435]. Most sequences (94–95%)
corresponded to annotated mRNA sequences. The
sequence redundancy within each library is low, 288 dif-
ferent genes are represented in the RGP library (non-
redundancy of 82%) and 217 in the Met library (non-
KISS1 and RHOC mRNA expression in a panel of RGP, VGP and metastatic melanoma cell linesFigure 1
KISS1 and RHOC mRNA expression in a panel of
RGP, VGP and metastatic melanoma cell lines. Com-
parison of the expression levels of the KISS1 metastasis sup-
pressor gene (A) and the small GTPase RHOC (B) among
melanoma cell lines of different stages of tumor progression
supported the selection of WM1552C cell line as the RGP
representative for suppression subtractive hybridization
against a pool of metastatic cell lines. Samples of 20 μg of
total RNA from different melanoma cell lines were submitted
to electrophoresis in 1% agarose-formaldehyde gel and trans-
ferred to nylon membrane (Hybond N, Amersham Pharmacia
Biotech) by standard methods. Fragments of the indicated
genes were radiolabeled with [α-32P]-dCTP by random-
priming (Rad-prime kit, Invitrogen) and used as probes for
Northern blot hybridization. In order to correct for loading
differences, after stripping, the blots were probed with a
ACTB (β-actin) cDNA fragment.
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redundancy of 59%). Additional File 2 lists the identifier
and annotation of the genes represented in the RGP
(Tables S1) and Met (Table S2) libraries and indicates the
number of sequence occurrences for each gene in the
library. Common to both libraries, there were 10 genes
represented by 22 sequences, which corresponded to only
2.5% of the total number of sequences analyzed (Table 1,
and Additional File 2). A total number of 24 and 19
sequences from the RGP and Met libraries, respectively,
match only EST (Expressed Sequence Tag) sequences in
the GenBank, thus suggesting that the libraries may con-
tain sequences representing rare human transcripts. In
addition, 8 RGP sequences and 5 Met sequences mapping
to the human genome sequence (Additional File 3, Fig.
S2–S14) do not match any expressed sequences and thus
they might represent novel transcripts. Even among the
sequences matching known human mRNAs we obtain
additional information. For example, a sequence from the
Met library aligns to and extends a putative alternative
exon of a cDNA corresponding to the gene ABCB5 (Addi-
tional File 3, Fig. S15).
Assuming that the genes represented by multiple clones
within each library are the ones with the highest differen-
tial expression levels between the RGP and metastatic cell
lines, we reviewed the literature on these genes by search-
ing for their involvement in cancer in general and specifi-
cally in melanoma, as summarized in the Additional File
4 (Tables S3 and S4). In the RGP library, 37 genes are rep-
resented by at least 2 sequences (maximum number of
clones for a gene is 10) and in the Met library this number
is 32 genes (maximum number of clones for a gene is 55).
Among these 37 genes from the RGP library, 18 have been
reported with some alteration in cancer (only 3 of them in
melanoma) whereas the 19 remaining have not been
associated to cancer. In the Met library, 23 from the 32
genes have been associated to cancer, including 10 also
associated to melanoma development.
Validation of the expression pattern of genes identified in
the subtractive libraries
The identification of only 2% of the clones shared by both
libraries strongly suggested that the cDNA subtraction was
highly efficient. However, to confirm that this was indeed
the case, we selected 7 genes for validation by Northern
blots and RT-PCR in a panel of 6–8 melanoma cell lines
that represent the three stages of tumor progression,
including the cell lines used for the SSH libraries (Fig. 3).
The genes selected for validation from the RGP library
were DCN (represented by 8 clones), ALS2CR7 (10
clones) and MBOAT1 (3 clones). DCN encodes decorin, a
secreted protein involved in cell growth regulation and
Subtracted cDNA profiles of the RGP and metastatic (Met) cellsFigure 2
Subtracted cDNA profiles of the RGP and metastatic
(Met) cells. PCR-1 represents the PCR products generated
using a single primer directed towards both adaptors, after
27 amplification cycles from two duplicate samples of sub-
tracted (S1 and S2) or non-subtracted (NS) cDNA of the
RGP (WM1552C) and the metastatic (a pool of WM9,
WM852, 1205Lu and WM1617) cell lines. PCR-2 represents
the PCR product generated after 10 amplification cycles by
nested-PCR using a specific primer for each adaptor. Note
the difference between the subtracted and non-subtracted
Table 1: Global analysis of the clones generated by Suppression Subtractive Hybridization
SSH collections RGP library Met library
Number of clones obtained 2016 1920
Sequences analyzed 367 (18.5%) 386 (20.5%)
Sequences matching human mRNAs/ESTs 351 (94.1%) 368 (95%)
Sequences matching introns 5 (1.3%) 1 (0.26%)
Chimerical clones 2 (0.5%) 2 (0.5%)
Sequences matching intergenic regions 4 (1.1%) 4 (1.0%)
Sequences matching mitochondrial genome 5 (1.3%) 11 (2.8%)
Number of genes represented 288 (82%) 217 (59%)
Genes represented by more than one clone 37 (11%) 32 (8.7%)
Sequences corresponding to genes common in both libraries 12 (2.2%) 10 (2.5%)
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apoptosis induction in tumors [43]. By Northern blot
(Fig. 3A), we detected DCN mRNA only in the WM1552C
cells and at very high levels, indicating that these libraries
represent genes highly differentially expressed. ALS2CR7,
a candidate gene of amyotrophic lateral sclerosis 2,
encodes a putative protein kinase, based on Gene Ontol-
ogy prediction, with no characterized function. As shown
in Fig. 3B, although some signal for ALS2CR7 mRNA
expression was detected in all eight cell lines analyzed, all
four metastatic cell lines presented equivalently low sig-
nals and the highest levels were detected in WM1552C,
confirming the differential expression of this gene identi-
fied in the RGP SSH library. The MBOAT1 gene encodes a
hypothetical transmembrane protein containing an O-
acyltransferase domain, also with no characterized func-
tion and as predicted by its occurrence in the RGP library,
we confirmed that its mRNA expression is higher in
WM1552C (Fig. 3C). On the other hand, when a fragment
of a gene identified in both libraries, YWHAZ (14.3.3ζ)
was used as probe for Northern blot hybridization (Fig.
3D), we detected average signals of similar intensity
between the RGP WM1552C and the metastatic cell lines,
although some variation in the expression of this gene can
be noted among the cell lines analyzed.
From the Met library, we selected the genes MITF (repre-
sented by one clone), PLP1 (24 clones) and HLA-DRA (55
clones). MITF is a transcription factor involved in melano-
cyte differentiation and survival and even though it is rep-
resented by only one sequence in the Met library, its
differential expression between the RGP WM1552C and
the metastatic cell lines was confirmed by Northern blot
(Fig. 3E). High levels of MITF mRNA were detected in
three of the cell lines independent of the growth phase,
the RGP WM35, the VGP WM902, and the metastatic
WM1617, but no signal was detected in the RGP
WM1552C cells. PLP1 encodes a proteolipid protein
involved in myelinization and as predicted by its presence
in the Met library, the RGP WM1552C cell line showed no
detectable expression of PLP1 mRNA while high levels
were detected in the metastatic cell lines (Fig. 3F). To val-
idate the expression pattern of the MHC class II HLA-DRA,
the most redundant gene found in the Met library, we per-
formed RT-PCR using cDNA from 6 cell lines and ana-
lyzed the amplified product at 25, 28, 30 and 32
amplification cycles on agarose gel (Fig. 3G). The data
confirmed high expression levels of HLA-DRA mRNA in
the WM1617 metastatic cell line and a weak expression in
the RGP cell line WM1552C (Fig. 3G). Moderate expres-
sion levels were detected in cell lines of different growth
phases (WM35, WM793 and WM852) and, interestingly,
1205Lu, which was selected in immunodefficient mouse
from WM793, shows low expression levels. Also, high lev-
els of the HLA-DRA protein were confirmed by flow
cytometry for WM1617 and WM9 (data not shown).
Therefore, the melanoma cell lines analyzed here express
differential levels of HLA-DRA but without showing corre-
lation to any particular phase of the tumor development.
In summary, we conclude that all of the genes selected for
validation confirmed the expression pattern predicted by
their presence in only one of the two libraries.
Genes of specific biological processes and from distinct
chromosome locations are differentially enriched between
the RGP and Met libraries
In order to verify if specific classes of proteins are differen-
tially enriched in the RGP or Met libraries, we submitted
the two total lists of genes identified in our SSH libraries
to a functional annotation based on the Gene Ontology,
according to the biological processes. The annotation was
performed using the software GOTM (Gene Ontology
Tree Machine) that also compare the frequency of genes in
each functional class with the expected frequency based
on the distribution of all human genes throughout the
GO functional classes. The GO functional classes that are
significantly enriched in the RGP and Met libraries, in
comparison to the distribution of all predicted human
genes, are listed in Tables 2 and 3. Genes corresponding to
proteins involved in nucleic acid binding are enriched in
both libraries, however the number of genes and proc-
esses related to this function is greater in the RGP library.
In the Met library, regulation of transcription and RNA
processing are the two processes involving nucleic acid
binding proteins that were considered enriched. In the
RGP library, we detected a large number of genes related
to DNA metabolism, DNA repair, chromatin remodeling
and RNA processing. In addition, proteins involved in
cytoskeleton processes related to subcellular transport and
localization, as well as proteins involved in macromole-
cule degradation are also enriched in the RGP library. On
the other hand, processes related to cell adhesion and cell
migration were considered specifically enriched in the
Met library. These processes include genes coding for
components of extracellular matrix and several types of
receptors such as G protein-coupled receptors, tyrosine
kinase receptors, integrins and nuclear receptors.
We also analyzed the chromosome location of all genes/
ESTs identified in the SSH libraries (Fig. 4). Interestingly,
genes mapping to chromosome 1 are much more repre-
sented in the RGP library (19%) than in the MET library
(4%). Also, at lower extent, chromosomes 2, 6 and 12 had
more genes identified in the RGP than in the Met library,
whereas genes from chromosomes 11 and 13 showed an
inverted pattern.
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Validation by Northern blot and RT-PCR of the expression pattern of seven genes identified in the SSH librariesFigure 3
Validation by Northern blot and RT-PCR of the expression pattern of seven genes identified in the SSH librar-
ies. Frames depict the names of cell lines used in the construction of the libraries. The inserts of cDNA clones corresponding
to the genes DCN (decorin) (A), ALS2CR7 (B) and MBOAT1 (C) of the RGP library; YWHAZ (14-3-3 ξ) (D) identified in both
libraries; and MITF (E) and PLP1 (F) from the Met library were isolated and used as probes for hybridization in Northern blots
containing total RNA from the melanoma cell lines indicated above the panels – Blank lanes mean that the corresponding cell
line was not included in the Northern blot, and were introduced to allow alignment among panels. Northern blots were pre-
pared as described in Figure 1. HLA-DRA (G) identified in the Met library was validated by RT-PCR. For RT-PCR, total RNA
samples (2 μg) from the indicated cell lines were, after DNase treatment, submitted to reverse transcription with Superscript
II (Invitrogen) using oligo dT as primer and the cDNA was used as template for PCR amplification with HLA-DRA primers. After
25, 28, 30 and 32 amplification cycles, 5 μl aliquots were collected for agarose gel electrophoresis. As endogenous control, a
pair of primers for the ACTB (β-actin) mRNA was used. C: Control RT-PCR amplification using as template RNA (DNase
treated) without prior reverse transcription.
BMC Cancer 2008, 8:19
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Analysis of the expression profile of the genes represented
in the SSH libraries in a panel of melanocytic samples using
a publicly available microarray study
Since our validation results indicate that we have two col-
lections of genes with truly differential expression
between an RGP and a pool of metastatic melanoma cell
lines, we decided to analyze the behavior of the subtracted
genes in a panel of human melanoma tumors. We
searched the data tables of a publicly available microarray
study [16] that contains the expression profiles of samples
from normal skin (3 samples), nevi (9 samples), primary
(6 samples) and metastatic (19 samples) melanomas
hybridized against a cDNA microarray containing 20,862
human cDNAs (representing 19,740 unique genes) from
Research Genetics (Huntsville, AL). This work showed
that metastatic melanomas exhibit two different gene
expression signatures and that one of these signatures is
shared with a sample from an RGP melanoma lesion. We
were able to extract expression data for 194 genes of the
RGP library and for 155 genes of the Met library. First, the
set of data for each group was submitted to SAM (Signifi-
cance Analysis of Microarray), setting the False Discovery
Rate (FDR) to zero, in a two-class unpaired analysis where
one group was represented by primary melanomas and
the other group by metastatic melanomas. As shown in
Fig. 5, a subset of RGP library genes was able to distin-
guish primary melanomas from metastasis, although two
metastatic samples grouped with the primary tumors. A
small group of genes from the RGP library were pointed as
differentially expressed between primary and metastatic
melanomas, including genes overexpressed in primary
tumors as well as genes overexpressed in metastatic
melanomas. The genes detected as down-regulated in
most metastatic melanomas, and that are therefore candi-
dates for metastasis suppressors, are LUM (lumican),
DCTN6 (dynactin 6) and DNCI2 (dynein intermediate
chain 2) (Fig. 5A). The Met library genes were not able to
distinguish between primary and metastatic melanomas,
since only one gene, ENDOD1 that codes for a putative
endonuclease, was detected by SAM, at FDR = 0, as differ-
entially expressed between the two tumor stages. In a sec-
ond analysis, we performed SAM to compare non-
Table 2: Functional classes of genes enriched in the RGP library in comparison to the frequency within the whole set of predicted
human genes
Functional Class (biological process) Genes Relative enrichment significance
establishment of cellular localization AP1G1, DYNC1I2, COPZ1, NUP160, KIF5B, RANBP5,
PAFAH1B1, FLJ10292, C14orf108, RAN, SET, SGNE1, SSR1,
O = 16
; E = 8.04
; R = 1.99
P = 0.0067
nucleocytoplasmic transport NUP160, RANBP5, FLJ10292, RAN, SET, BAT1 O = 6; E = 1.61; R = 3.73; P = 0.0054
organelle organization and biogenesis ARPC3, DCTN6, MYST2, DDX1, DYNC1I2, XRN2, KIFAP3,
O = 24; E = 10.92; R = 2.2; P = 0.00022
chromosome organization and biogenesis MYST2, POT1, H3F3A, HDAC1, HMGB2, ATRX, SET,
O = 10; E = 4.05; R = 2.47; P = 0.0076
microtubule-based process DYNC1I2, XRN2, KIFAP3, KIF5B, STMN1, PAFAH1B1, RAN O = 7; E = 2.13; R = 3.29; P = 0.0056
nucleobase biosynthesis PAICS, PPAT O = 2; E = 0.12; R = 16.67; P = 0.0064
regulation of protein biosynthesis DDX1, EIF4B, EIF4G2, PUM2, TLR3, EIF4E2 O = 6; E = 1.66; R = 3.61; P = 0.0064
DNA metabolism POLD3, MYST2, XRN2, POT1, H3F3A, HDAC1, HMGB2,
O = 19; E = 8.44; R = 2.25; P = 0.00078
DNA packaging MYST2, H3F3A, HDAC1, HMGB2, SET, SMYD3, HAT1,
O = 9; E = 3.42; R = 2.63; P = 0.0074
O = 8; E = 2.91; R = 2.75; P = 0.0088
response to DNA damage stimulus POLD3, XRN2, HMGB2, NONO, ZAK, ATRX, RAD23B,
O = 9; E = 3.24; R = 2.78; P = 0.0053
RNA metabolism SYNCRIP, DDX17, SF3A3, DDX1, DCP2, ELAVL1, XRN2,
O = 19; E = 5.8; R = 3.28; P = 5.44-06
RNA processing SYNCRIP, DDX17, SF3A3, DDX1, XRN2, SF3B1, LSM5,
O = 16; E = 4.68; R = 3.42; P =
FLJ10292, SNRPG, BAT1, TTF2, SIP1, DDX23
O = 13; E = 2.01; R = 6.47; P = 1.03E-07
RNA localization NUP160, FLJ10292, RAN, BAT1 O = 4; E = 0.64; R = 6.25; P = 0.0037
macromolecule catabolism YME1L1, DDX1, DCP2, ELAVL1, XRN2, USP33, ARIH1,
O = 14; E = 4.57; R = 3.06 P = 0.00019
a: observed number of genes in the category; b: expected number of genes in the category; c: observed/expected ratio; d: p-value of the enrichment
BMC Cancer 2008, 8:19
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neoplastic samples (skin and nevi) against tumor samples
(primary and metastatic melanomas) for each set of genes
(Fig. 5B and 5C). Genes from both libraries were found
differentially expressed between non-neoplastic tissues
and melanoma in both directions (up-regulated in non-
neoplastic tissue/down-regulated in tumor, and down-
regulated in non-neoplastic tissue/up-regulated in
tumor). Since the WM1552C cells display markers of a
less aggressive phenotype, for the RGP gene collection, we
indicate a group of 14 genes (Fig. 5B, left side blue line)
that were found to be consistently down-regulated in
tumor samples. In contrast, a set of 14 Met library genes
(Fig. 5C, left side blue line) showed a consistent up-regu-
lation in tumor samples, compatible with an oncogenic
role. The consistent differential expression profiles of
these two groups of genes, distinguishing non-neoplastic
from neoplastic tissues and primary melanomas from
metastatic ones, make them good candidates for further
studies in melanoma.
Treeview analysis of the genes validated by Northern blots
showed that DCN is preferentially overexpressed in most
skin and nevi samples and in 2 (out of 5) primary melano-
mas, confirming that its expression seems to decrease with
melanoma progression, although a subset of melanoma
samples also presented an overexpression of this gene
(Fig. 5C, bottom). MITF, which was identified in the Met
library, showed down-regulation in all skin samples, an
increased expression in most nevi and overexpression in
11 out of 25 samples of primary and metastatic melano-
mas (Fig. 5C, bottom). The other two genes from the Met
library (PLP-1 and HLA-DRA) did not exhibit expression
pattern clearly associated to any specific stage of the
melanoma progression in this study (Fig. 5C, bottom).
We report here the generation and analysis of two collec-
tions of subtracted cDNAs corresponding mostly to anno-
tated mRNA. The collections are unique with only a few of
the clones (2.5%) being common to both libraries, indi-
cating that these collections represent a transcriptional
content that distinguish between the RGP cells WM1552C
and a group of four metastatic cell lines, and may reflect
distinct transcriptional profiles of these two stages of
melanoma progression (Additional File 2, Tables S1 and
S2). Some of the sequences identified also contribute to
extend annotated mRNA, as is the case for ABCB5 (Fig.
S15), or reveal novel transcripts, since they match only
genomic DNA, mapping in introns or intergenic regions
(Additional File 3, Fig. S2–S14). We further assured that
the subtraction process was efficient and validated the
libraries by showing that all genes selected for validation
Table 3: Functional classes of genes enriched in the Met library in comparison to the frequency within the whole set of predicted
human genes
Functional Class (biological process) Genes Relative enrichment significance
cell adhesion ADAM10, CTGF, CTNNB1, CTNND1, FN1, ITGA6,
O = 19
; E = 6.7
; R = 2.84
; P = 3.71E-05
cell-matrix adhesion ITGA6, ITGB1, ITGB8, SPP1 O = 4; E = 0.6; R = 6.67; P = 0.0030
regulation of cell adhesion ADAM10, LAMA4, TGFBI, CD164 O = 4; E = 0.4; R = 10; P = 0.00064
integrin-mediated signaling pathway ADAM10, ITGA6, ITGB1, ITGB8 O = 4; E = 0.57; R = 7.02; P = 0.0025
intracellular receptor-mediated signaling pathway CTNNB1, EDD1, RB1, NCOA4 O = 4; E = 0.47; R = 8.51; P = 0.0012
cell differentiation ACVR1C, DCT, GPM6B, MGP, MITF, NRCAM,
O = 13; E = 5.34; R = 2.43; P = 0.0027
cell motility CTGF, FN1, ITGB1, LAMA4, NRCAM, SERPINE2,
O = 9; E = 2.42; R = 3.72; P = 0.00072
cell migration FN1, ITGB1, LAMA4, NRCAM, SERPINE2, SPP1,
O = 8; E = 1.04; R = 7.69; P = 9.41E-06
nucleocytoplasmic transport ADAM10, KPNA1, NPM1, IPO9, G3BP2, THOC1 O = 6; E = 1.25; R = 4.8; P = 0.0016
negative regulation of cell proliferation GPNMB, FABP7, IL6, NPM1, CUL5, CD164 O = 6; E = 1.59; R = 3.77; P = 0.0052
aromatic amino acid family metabolism DCT, TDO2, TYR, TYRP1 O = 4; E = 0.23; R = 17.39; P = 6.642E-05
aromatic compound metabolism CPM, DCT, TDO2, TYR, TYRP1 O = 5; E = 0.97; R = 5.15; P = 0.0028
cofactor biosynthesis PBEF1, TMEM131, TPK1, ATP5A1, ATP6V1B2 O = 5; E = 1.18; R = 4.24; P = 0.0066
coenzyme biosynthesis PBEF1, TMEM131, TPK1, ATP5A1, ATP6V1B2 O = 5; E = 1.04; R = 4.81; P = 0.0039
negative regulation of transcription HMGB1, TRIM33, HBXAP, NKRF, RB1, ARID5B O = 6; E = 1.72; R = 3.49; P = 0.0076
positive regulation of transcription CTNNB1, ILF2, NFATC2, HBXAP, RB1, NCOA4 O = 6; E = 1.19; R = 5.04; P = 0.0012
mRNA processing DHX8, PABPC1, GRSF1, SFRS2, SNRPB2, SNRPG,
O = 8; E = 2.13; R = 3.76; P = 0.0013
pigment metabolism DCT, TYR, TYRP1 O = 3; E = 0.25; R = 12; P = 0.0018
a: observed number of genes in the category; b: expected number of genes in the category; c: observed/expected ratio; d: p-value of the enrichment
BMC Cancer 2008, 8:19
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by other methods confirmed the differential expression
between the metastatic and the WM1552C cells (Fig. 3).
Although some subtracted genes might reflect only indi-
vidual genetic variations due to the use of a single cell line
representing the RGP stage, we believe many of them truly
represent cancer associated genes, since among them we
have several known genes with characterized cancer
related functions (Additional files 2 and 4).
Potential metastasis suppressor pathways
Genes involved in DNA packaging, DNA repair and
response to DNA damage are particularly enriched in the
RGP library (Table 2), in accordance to the fact that early
tumorigenic lesions have an activated DNA damage
response and, in contrast to advanced tumors, are not
marked by gross genomic instability [44,45]. The dispro-
portion in the number of genes mapping to different chro-
mosomes between the two libraries (Fig. 4) may be
explained at least in part by chromosome abnormalities,
which have been described for two of the cell lines used
here, WM1552C (translocations involving 1p22, 5q34,
11p11, 12q11) and WM9 (loss of the long arm of chro-
mosome 6 and gain of an extra copy of the entire chromo-
some 7) [7]. Since no extra copy of chromosome 1 occurs
Genes from distinct chromosome locations are differentially enriched between the RGP and Met librariesFigure 4
Genes from distinct chromosome locations are differentially enriched between the RGP and Met libraries.
Chromosome locations of all genes/ESTs were obtained from GenBank accession number reports or through BLAT alignment.
(A) Represents the total number of genes per human chromosome for each library; (B and C) Represent the chromosome
locations for all genes identified in the RGP (B) and Met (C) libraries, along the length (bp) of all human chromosomes. The
absence of genes mapping to Y chromosome in the RGP library is not explained by lack of this chromosome since the RGP cell
line WM1552C was obtained from a male patient.
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Genes identified in the SSH library distinguish non-neoplastic from neoplastic tissues and primary from metastatic melanomas in a microarray study on melanoma progressionFigure 5
Genes identified in the SSH library distinguish non-neoplastic from neoplastic tissues and primary from meta-
static melanomas in a microarray study on melanoma progression. Expression data from the microarray analysis by
Haqq et al [16] were collected for the genes identified in the RGP and Met libraries. The expression data for each gene group
were submitted to SAM (FDR = 0) in a two-class analysis for detection of genes differentially expressed between primary and
metastatic tumors and between non-neoplastic (skin and melanocytic nevi) and neoplastic (primary and metastatic melanomas)
samples. The results from SAM analysis were extracted using SAMTERS and visualized by CLUSTER 3.0 and Java TreeView –
Red and green squares represent genes up-regulated and down-regulated, respectively. (A) Expression profiles from primary
and metastatic tumors for genes from the RGP library. (B – C) Expression profiles from non-neoplastic and neoplastic samples
for genes from the RGP (B) and Met (C) libraries. Vertical blue lines on the left side indicate: (B) Genes from the RGP library
that showed up-regulation in non-neoplastic samples; and (C) Genes from the Met library up-regulated in neoplastic tissues.
BMC Cancer 2008, 8:19
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in WM1552C, the enrichment of chromosome 1 genes in
the RGP library might be explained by losses involving
this chromosome in the metastatic cell lines. Indeed, loss
of the long arm and translocation involving chromosome
1 was observed in cell lines that are paired with the meta-
static cell lines WM1617 and 1205Lu [6,46]. This is in
agreement with the frequent rearrangements found in
chromosome 1 in advanced melanomas [46-48].
Several of the genes identified in the RGP library have
been reported as anti-tumorigenic or anti-metastatic, as
for instance, DCN [43], ANLN [49], HMGB2 [50],
CXCL11 [51]. Some of them are potentially involved in
the KISS1 pathway, since KISS1 expression was detected
only in WM1552C cells (Fig. 1). The KISS1 gene product,
the secreted protein kisspeptin/metastin, plays an inhibi-
tory role in chemotaxis and invasion of melanoma cells
by a mechanism involving remodeling of the actin
cytoskeleton [23]. Interestingly, many genes in the RGP
library encode proteins associated to the actin cytoskele-
ton (see Additional File 2, Table S1 and Additional File 4,
Table S3). Besides its role as a metastasis suppressor [20],
first revealed in a melanoma model using SSH approach,
kisspeptin/metastin and its receptor GPR54/KISS1R were
recently implicated as important triggers of the complex
process of sexual maturation [52,53]. The hormone leptin
and its receptor also play important role in this process
since leptin is a permissive factor for pubertal develop-
ment [54], and the possibility that leptin modulates KISS1
expression in the central nervous system is under investi-
gation [55]. In this context, the identification of LEPR
gene, which codes for the leptin receptor, in the RGP
library (Additional File 2, Table S1) might bear some rel-
evance. Another gene with expression pattern similar to
the one shown for KISS1, i.e., expressed exclusively in
WM1552C (Fig. 3), is DCN. Both gene products, kisspep-
tin and decorin, besides having anti-migratory and anti-
invasive roles in tumor cells are also implicated as regula-
tors in the process of trophoblast invasion [56,57]. LUM,
whose expression is down regulated in most metastatic
melanomas (Fig. 5), is another member of the family of
small proteoglycans that includes decorin for which an
anti-invasive role was reported [58]. Therefore, novel
genes with potential role in the maintenance of a status of
low aggressiveness are likely to be represented in the RGP
library, and some of them might be directly or indirectly
associated to the KISS1 metastasis-suppressor pathway.
Of note, detection of WNT5A in the RGP library is in
marked contrast to previous evidence that WNT5A is
strongly associated with aggressiveness in human
melanoma [59] and also to a most recent finding that
KISS1 expression is down-regulated by Wnt5a [60]. This
raises the interesting possibility that WM1552C cells carry
an inactivating mutation that affects the Wnt5a signaling
Two of the RGP genes (ALS2CR7 and MBOAT1) validated
by Northern blots encode proteins of unknown function,
both highly expressed in WM1552C compared to VGP
and metastatic cell lines making them interesting candi-
dates for further investigations. This is the first evidence
for the expression of these genes in melanocytic cells, and
points towards a role in melanoma development.
Potential oncogenic pathways
Among the genes represented in the Met library, many are
associated to tumor growth, invasiveness and metastasis,
such as TM4SF1 [61], LAMA4 [62], G3BP2 [63], CD59
antigen [64], and SPP1 [65]. Genes encoding proteins
involved in the control of cell adhesion and cell migration
are enriched in the Met library (Table 3). Among these
genes, we have several growth factor receptors and
integrins, including ITGB8 whose relevance in cancer
remains to be characterized. Among the validated Met
genes (HLA-DRA, PLP1 and MITF), several lines of evi-
dence suggest their involvement in tumorigenesis. HLA-
DRA encodes the α chain of the HLA-DR, one of the MHC
class II molecules that, in contrast to MHC class I mole-
cules, are not normally expressed by nonprofessional anti-
gen-presenting cells (APC). Functional MHC class II
molecules, key initiators of an immune response by acti-
vating CD4+ naïve T cells, are heterodimeric proteins
composed of α and β chains encoded by separated genes
(A and B). Melanocytes from normal skin and common
nevus are negative for HLA class II molecules [66,67]
while both primary and metastatic melanomas display
heterogeneous levels of positive cells [67,68]. Although
melanoma cells acquire HLA-DRA expression during
tumor development, the prognostic value of this expres-
sion has not been clarified [68-73]. In contrast to the over-
expression of HLA-DR α chain in several cancers, the HLA-
DR β chain is not frequently overexpressed in cancer, sug-
gesting that cancer cells do not express a functional HLA-
DR receptor [74]. Clones corresponding to the HLA-DRA
gene were the most abundantly sequenced clones from
the Met library. However, sequences corresponding to the
HLA-DRB gene were not detected in the same library, sug-
gesting that HLA-DRA may not form a functional antigen-
presenting molecule in these cells.
PLP1 is a transmembrane protein involved in myeliniza-
tion [75] whose up regulation was detected in leiomyo-
mas [76] and melanoma cell lines [77], and although no
previous study addresses the role of PLP1 in melanoma
development, remarkably, we show here that all vertical
growth phase and metastatic cell lines exhibit high expres-
sion levels of this gene. It is interesting to note that PLP1
expression is regulated by the transcription factor SOX10
BMC Cancer 2008, 8:19
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[78], which is also implicated in the regulation of MITF
[79], the other validated gene.
MITF encodes a transcription factor required for melano-
cyte differentiation and survival [80,81] and we con-
firmed high expression levels of MITF mRNA in one of the
metastatic cell lines, WM1617, which also showed the
highest level of PLP1 expression. The high levels of MITF
in WM1617 cells must explain the occurrence of many
melanocytic markers (known MITF targets) in the Met
library, as TYRP1, TYR, DCT, MLANA. However, differ-
ently from PLP1, high levels of MITF mRNA expression
were not detected in the other three metastatic cell lines,
but rather in cell lines from RGP and VGP tumor stages
(WM35 and WM902). Consistent with this result, micro-
array studies revealed MITF overexpression in a subset of
primary and metastatic melanoma samples [16,77] and
led to the proposal [77] of a classification of melanoma
cell lines independently of tumor stage. Interestingly, in
the latter study, PLP1, MITF and the melanocytic markers
were all detected as co-regulated genes associated to high
proliferation and low metastatic potential in groups of
cell lines that included WM1617 and WM35. So it is likely
that a set of the genes detected in the Met library is more
importantly linked to tumor proliferation/survival than
Although MITF is able to induce cell cycle arrest in
melanocytes and melanoma cells in a p16 and p21
dependent manner [82,83], MITF gene was found to be
amplified in melanoma and its overexpression induced
transformation and drug resistance in BRAF mutant
melanocytes [84]. In addition, recent works have identi-
fied as MITF transcriptional targets genes such as the
CDK2 [85], the hipoxia induced factor HIF1A [86] and the
hepatocyte growth factor receptor MET [87], all of them
presenting functions that contribute to tumor develop-
ment. Thus, the cellular circuits in which MITF is engaged
are clearly complex and when and how MITF contributes
to melanoma development are open questions. Since
many of the known MITF targets are represented in the
Met library, this collection may contain novel MITF tar-
gets and co-regulated genes whose identification will
probably contribute to shed light on the MITF participa-
tion in melanoma development.
Other genes with consistent overexpression in tumor sam-
ples in comparison to nevi and skin (Fig. 5C) compatible
with an oncogenic role include genes already associated to
melanoma progression, such as FN1, which codes for the
matrix protein fibronectin [88]; genes with proposed roles
in tumor growth and metastasis, such as ADAM10, which
encodes a putative desintegrin metallopeptidase [89],
SGK, which encodes a glucocorticoid regulated kinase
[90], NRP2, which encodes neuropilin 2[91], and also
genes with no characterized function or association to
cancer, such as C18orf19.
When we looked for the expression profile for RGP and
Met genes in the microarray data obtained by Hoek et al
[92], 13 and 19 genes, respectively, were found amongst
the genes up-regulated in melanoma cell lines as com-
pared to melanocytes (Additional File 5, Tables S5 and
S6). Eleven genes (Additional File 6, Table S7) from our
subtractive libraries were also detected as differentially
expressed between murine tumorigenic melanoma cells
and a parental nontumoral cell line in a study using pro-
teomics and SAGE analysis [93]. Up-regulation of one of
these genes, NPM1 (nucleophosmin), was found at both
protein and transcript levels in melanoma cells, compati-
ble with the detection of this gene in our Met library. An
accumulation of a specific form of the nucleophosmin
protein was also detected in human melanoma cell lines
(including several of the WM cells used here) in compari-
son to normal melanocytes in another proteomics study
Altogether the data shown here strengthen previous evi-
dence for several genes as candidate markers for
melanoma progression and suggest that the subtractive
libraries described are enriched in cancer-related genes,
representing validated tools to be used in future studies
for the identification of novel genes or pathways involved
in melanoma progression.
Competing interests
The author(s) declare that they have no competing inter-
Authors' contributions
JFS designed the study, constructed, and sequenced the
SSH libraries, conducted all expression validation and
comparisons, and drafted the manuscript. EME conceived
the study, participated in its design and coordination and
also drafted and revised the manuscript. Both authors
read and approved the final manuscript.
Additional material
Additional file 1
Representative image of agarose gels containing PCR-amplified inserts
from 96 randomly selected clones from the subtractive libraries. The
images show that most clones from both RGP and Met libraries carry
inserts and most of them are
600 bp in length.
Click here for file
BMC Cancer 2008, 8:19
Page 15 of 18
(page number not for citation purposes)
We are especially grateful to Dr Meenhard Herlyn (Wistar Institute, Phila-
delphia, PE, USA) for the collection of human melanoma cell lines that he
kindly provided to us. We are thankful to Michel Mozinho dos Santos e Raul
Torrieri for assistance with the bioinformatics analysis; Silmara Reis Banzi,
Benedita Oliveira Souza and Sarah Cristina Freitas de Mello for technical
assistance, as well as to Dr Marcelo Brocchi (former professor at our
department, now at UNICAMP) and Dr Marco Antonio Zago (Hemocen-
tro de Ribeirão Preto/SP) for making sequencing machines available to our
project. We thank FAPESP, CNPq and FAEPA for research support to our
laboratory and CNPq for a post-doctorate fellowship to J.F.S. during 2006
and a current research fellowship to E.M.E. Also, we thank FAPESP for a
current post-doctorate fellowship to J.F.S.
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Additional file 2
Complete lists of the genes identified in the RGP and Met libraries. The
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Click here for file
Additional file 3
Schematic representations of the sequence alignments of ESTs from the
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Click here for file
Additional file 4
Lists of the genes represented by redundant clones in the RGP and Met
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Additional file 5
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Click here for file
Additional file 6
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Click here for file
BMC Cancer 2008, 8:19
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    • "Preclinical and clinical data support differential metastatic potential amongst tumors cells262728. Our current analysis, based on the rate of progression of metastasis emphasizes growth properties of different metastatic clones29303132. However, differences in the ability to colonize the lung microenvironment following primary tumor resection may also account for observed differences between oligo-and poly-metastatic phenotypes [33]. "
    [Show abstract] [Hide abstract] ABSTRACT: Rationale Strategies to stage and treat cancer rely on a presumption of either localized or widespread metastatic disease. An intermediate state of metastasis termed oligometastasis(es) characterized by limited progression has been proposed. Oligometastases are amenable to treatment by surgical resection or radiotherapy. Methods We analyzed microRNA expression patterns from lung metastasis samples of patients with ≤5 initial metastases resected with curative intent. Results Patients were stratified into subgroups based on their rate of metastatic progression. We prioritized microRNAs between patients with the highest and lowest rates of recurrence. We designated these as high rate of progression (HRP) and low rate of progression (LRP); the latter group included patients with no recurrences. The prioritized microRNAs distinguished HRP from LRP and were associated with rate of metastatic progression and survival in an independent validation dataset. Conclusion Oligo- and poly- metastasis are distinct entities at the clinical and molecular level.
    Full-text · Article · Dec 2012
    • "Microarray studies conducted to discover molecular pathways linked with tumour progression and papers comparing metastases to initial tumours have already been performed [37]–[41]. The use of paired samples alleviate the bias associated with interindividual variation; for example, comparison of expression profile of tumours prior to and following systemic chemotherapy allowed the identification of differentially expressed genes correlated with chemoresistance in ovarian carcinomas. "
    [Show abstract] [Hide abstract] ABSTRACT: Children with ependymoma may experience a relapse in up to 50% of cases depending on the extent of resection. Key biological events associated with recurrence are unknown. To discover the biology behind the recurrence of ependymomas, we performed CGHarray and a dual-color gene expression microarray analysis of 17 tumors at diagnosis co-hybridized with the corresponding 27 first or subsequent relapses from the same patient. As treatment and location had only limited influence on specific gene expression changes at relapse, we established a common signature for relapse. Eighty-seven genes showed an absolute fold change ≥2 in at least 50% of relapses and were defined as the gene expression signature of ependymoma recurrence. The most frequently upregulated genes are involved in the kinetochore (ASPM, KIF11) or in neural development (CD133, Wnt and Notch pathways). Metallothionein (MT) genes were downregulated in up to 80% of the recurrences. Quantitative PCR for ASPM, KIF11 and MT3 plus immunohistochemistry for ASPM and MT3 confirmed the microarray results. Immunohistochemistry on an independent series of 24 tumor pairs at diagnosis and at relapse confirmed the decrease of MT3 expression at recurrence in 17/24 tumor pairs (p = 0.002). Conversely, ASPM expression was more frequently positive at relapse (87.5% vs 37.5%, p = 0.03). Loss or deletion of the MT genes cluster was never observed at relapse. Promoter sequencing after bisulfite treatment of DNA from primary tumors and recurrences as well as treatment of short-term ependymoma cells cultures with a demethylating agent showed that methylation was not involved in MT3 downregulation. However, in vitro treatment with a histone deacetylase inhibitor or zinc restored MT3 expression. The most frequent molecular events associated with ependymoma recurrence were over-expression of kinetochore proteins and down-regulation of metallothioneins. Metallothionein-3 expression is epigenetically controlled and can be restored in vitro by histone deacetylase inhibitors.
    Full-text · Article · Sep 2010
    • "Quantification of ABCB5 staining intensity of an established melanocytic tumor progression tissue microarray revealed that primary or metastatic melanomas expressed significantly more ABCB5 than benign melanocytic nevi, thick primary melanomas more than thin primary melanomas, and melanomas metastatic to lymph nodes more than primary lesions [6]. Consistent with these findings, the ABCB5 gene is also preferentially expressed by melanomas with high in vivo tumorigenic capacity in human to murine xenotransplantation models [24,25] and by melanomas of metastatic as opposed to primary tumor origin [26]. Thus, ABCB5 provides a direct and unique link between CSCs, cancer therapeutic resistance, and neoplastic progression in human malignant melanoma. "
    [Show abstract] [Hide abstract] ABSTRACT: Cancer stem cells (CSCs), also known as tumor-initiating cells, have been identified in several human malignancies, including human malignant melanoma. The frequency of malignant melanoma-initiating cells (MMICs), which are identified by their expression of ATP-binding cassette (ABC) family member ABCB5, correlates with disease progression in human patients. Furthermore, targeted MMIC ablation through ABCB5 inhibits tumor initiation and growth in preclinical xenotransplantation models, pointing to potential therapeutic promise of the CSC concept. Recent advances also show that CSCs can exert pro-angiogenic roles in tumor growth and serve immunomodulatory functions related to the evasion of host anti-tumor immunity. Thus, MMICs might initiate and sustain tumorigenic growth not only as a result of CSC-intrinsic self-renewal, differentiation and proliferative capacity, but also based on pro-tumorigenic interactions with the host environment.
    Article · Feb 2010
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